- Distributed and cloud computing systems are built over a large number of autonomous computer nodes.
- These node machines are interconnected by SANs, LANs, or WANs in a hierarchical manner.
- With today’s networking technology, a few LAN switches can easily connect hundreds of machines as a working cluster.
- A WAN can connect many local clusters to form a very large cluster of clusters. In this sense, one can build a massive system with millions of computers connected to edge networks.
Massive systems are considered highly scalable, and can reach web-scale connectivity, either physically or logically.
Massive systems are classified into four groups:
Clusters, P2P networks, Computing grids, and Internet clouds
In terms of node number, these four system classes may involve hundreds, thousands, or even millions of computers as participating nodes. These machines work collectively, cooperatively, or collaboratively at various levels. The table entries characterize these four system classes in various technical and application aspects.
Clusters of Cooperative Computers
A computing cluster consists
of interconnected stand-alone computers which work cooperatively as a single
integrated computing resource.
Cluster Architecture
This network can be as simple as a SAN (e.g., Myrinet) or a LAN (e.g., Ethernet). To build a larger cluster with more nodes, the interconnection network can be built with multiple levels of Gigabit Ethernet, Myrinet, or InfiniBand switches. Through hierarchical construction using a SAN, LAN, or WAN, one can build scalable clusters with an increasing number of nodes. The cluster is connected to the Internet via a virtual private network (VPN) gateway. The gateway IP address locates the cluster. The system image of a computer is decided by the way the OS manages the shared cluster resources. Most clusters have loosely coupled node computers. All resources of a server node are managed by their own OS. Thus, most clusters have multiple system images as a result of having many autonomous nodes under different OS control.
Single-System Image
An ideal cluster should merge multiple system images into a single-system image (SSI). Cluster designers desire a cluster operating system or some middle-ware to support SSI at various levels, including the sharing of CPUs, memory, and I/O across all cluster nodes. An SSI is an illusion created by software or hardware that presents a collection of resources as one integrated, powerful resource. SSI makes the cluster appear like a single machine to the user. A cluster with multiple system images is nothing but a collection of independent computers.
Hardware, Software, and Middleware Support
Clusters exploring massive parallelism are commonly known as MPPs. Almost all HPC clusters in the Top 500 list are also MPPs. The building blocks are computer nodes (PCs, workstations, servers, or SMP), special communication software such as PVM or MPI, and a network interface card in each computer node. Most clusters run under the Linux OS. The computer nodes are interconnected by a high-bandwidth network (such as Gigabit Ethernet, Myrinet, InfiniBand, etc.).Special
cluster middleware supports are needed to create SSI or high availability (HA). Both sequential and
parallel applications can run on the cluster, and special parallel environments
are needed to facilitate use of the cluster resources. For example, distributed
memory has multiple images. Users may want all distributed memory to be shared
by all servers by forming distribu-ted
shared memory (DSM). Many SSI features are expensive or difficult to achieve at
various cluster operational levels.
Instead of achieving SSI, many clusters are loosely coupled machines. Using
virtualization, one can build many virtual clusters dynamically, upon user
demand.
Major Cluster Design Issues
Unfortunately, a cluster-wide OS for complete resource sharing is not available yet. Middleware or OS extensions were developed at the user space to achieve SSI at selected functional levels. Without this middleware, cluster nodes cannot work together effectively to achieve cooperative computing. The software environments and applications must rely on the middleware to achieve high performance.
Grid Computing Infrastructures
In the past 30 years, users have experienced a natural growth path from Internet to web and grid computing services. Internet services such as the Telnet command enables a local computer to connect to a remote computer. A web service such as HTTP enables remote access of remote web pages. Grid computing is envisioned to allow close interaction among applications running on distant computers simultaneously. Forbes Magazine has projected the global growth of the IT-based economy from $1 trillion in 2001 to $20 trillion by 2015. The evolution from Internet to web and grid services is certainly playing a major role in this growth.
Computational Grids
Like an electric utility power grid, a computing grid offers an infrastructure that couples computers, software/middleware, special instruments, and people and sensors together. The grid is often constructed across LAN, WAN, or Internet backbone networks at a regional, national, or global scale. Enterprises or organizations present grids as integrated computing resources. They can also be viewed as virtual platforms to support virtual organizations. The computers used in a grid are primarily workstations, servers, clusters, and supercomputers. Personal computers, laptops, and PDAs can be used as access devices to a grid system.
The resource sites offer complementary computing resources, including workstations, large servers, a mesh of processors, and Linux clusters to satisfy a chain of computational needs. The grid is built across various IP broadband networks including LANs and WANs already used by enterprises or organizations over the Internet. The grid is presented to users as an integrated resource pool as shown in the upper half of the figure.
Special instruments may be
involved such as using the radio telescope in SETI@Home search of life in the
galaxy and the austrophysics@Swineburne for pulsars. At the server end, the
grid is a network. At the client end, we see wired or wireless terminal
devices. The grid integrates the computing, communication, contents, and
transactions as rented services. Enterprises and consumers form the user base,
which then defines the usage trends and service characteristics.
Grid Families
Grid technology demands new distributed computing models, software/middleware support, network protocols, and hardware infrastructures. National grid projects are followed by industrial grid plat-form development by IBM, Microsoft, Sun, HP, Dell, Cisco, EMC, Platform Computing, and others. New grid service providers (GSPs) and new grid applications have emerged rapidly, similar to the growth of Internet and web services in the past two decades.
Peer-to-Peer Network Families
An example of a well-established distributed system is the client-server architecture. In this scenario, client machines (PCs and workstations) are connected to a central server for compute, e-mail, file access, and database applications. The P2P architecture offers a distributed model of networked systems. First, a P2P network is client-oriented instead of server-oriented. In this section, P2P systems are introduced at the physical level and overlay networks at the logical level.
P2P Systems
In a P2P system, every node acts as both a client and a server, providing part of the system resources. Peer machines are simply client computers connected to the Internet. All client machines act autonomously to join or leave the system freely. This implies that no master-slave relationship exists among the peers. No central coordination or central database is needed. In other words, no peer machine has a global view of the entire P2P system. The system is self-organizing with distributed control.
Overlay Networks
Data items or files are distributed in the participating peers. Based on communication or file-sharing needs, the peer IDs form an overlay network at the logical level. This overlay is a virtual network formed by mapping each physical machine with its ID, logically, through a virtual mapping as shown in Figure 1.17. When a new peer joins the system, its peer ID is added as a node in the overlay network. When an existing peer leaves the system, its peer ID is removed from the overlay network automatically. Therefore, it is the P2P overlay network that characterizes the logical connectivity among the peers.
There are two types of overlay networks: unstructured and structured.
An unstructured overlay network is characterized by a random graph. There is no fixed route to send messages or files among the nodes. Often, flooding is applied to send a query to all nodes in an unstructured overlay, thus resulting in heavy network traffic and nondeterministic search results.
Structured overlay net-works follow certain connectivity topology and rules for inserting and removing nodes (peer IDs) from the overlay graph. Routing mechanisms are developed to take advantage of the structured overlays.
P2P Application Families
Based on application, P2P networks are classified into four groups, as shown in Table 1.5. The first family is for distributed file sharing of digital contents (music, videos, etc.) on the P2P network. This includes many popular P2P networks such as Gnutella, Napster, and BitTorrent, among others. Colla-boration P2P networks include MSN or Skype chatting, instant messaging, and collaborative design, among others. The third family is for distributed P2P computing in specific applications. For example, SETI@home provides 25 Tflops of distributed computing power, collectively, over 3 million Internet host machines. Other P2P platforms, such as JXTA, .NET, and FightingAID@home, support naming, discovery, communication, security, and resource aggregation in some P2P applications.
P2P Computing Challenges
P2P computing faces three types of heterogeneity problems in hardware, software, and network requirements. There are too many hardware models and architectures to select from; incompatibility exists between software and the OS; and different network connections and protocols make it too complex to apply in real applications. We need system scalability as the workload increases. System scaling is directly related to performance and bandwidth. P2P networks do have these properties. Data location is also important to affect collective performance. Data locality, network proximity, and interoperability are three design objectives in distributed P2P applications.
P2P performance is affected by routing efficiency and self-organization by participating peers. Fault tolerance, failure management, and load balancing are other important issues in using overlay networks. Lack of trust among peers poses another problem. Peers are strangers to one another. Security, privacy, and copyright violations are major worries by those in the industry in terms of applying P2P technology in business applications [35]. In a P2P network, all clients provide resources including computing power, storage space, and I/O bandwidth. The distributed nature of P2P net-works also increases robustness, because limited peer failures do not form a single point of failure.
By replicating data in multiple peers, one can easily lose data in failed nodes. On the other hand, disadvantages of P2P networks do exist. Because the system is not centralized, managing it is difficult. In addition, the system lacks security. Anyone can log on to the system and cause damage or abuse. Further, all client computers connected to a P2P network cannot be considered reliable or virus-free. In summary, P2P networks are reliable for a small number of peer nodes. They are only useful for applica-tions that require a low level of security and have no concern for data sensitivity. We will discuss P2P networks in Chapter 8, and extending P2P technology to social networking in Chapter 9.
Cloud Computing over the Internet
Gordon Bell, Jim Gray, and Alex Szalay [5] have advocated: “Computational science is changing to be data-intensive. Supercomputers must be balanced systems, not just CPU farms but also petascale I/O and networking arrays.” In the future, working with large data sets will typically mean sending the computations (programs) to the data, rather than copying the data to the workstations. This reflects the trend in IT of moving computing and data from desktops to large data centers, where there is on-demand provision of software, hardware, and data as a service. This data explosion has promoted the idea of cloud computing.
Cloud computing has been defined differently by many users and designers. For example, IBM, a major player in cloud computing, has defined it as follows: “A cloud is a pool of virtualized computer resources. A cloud can host a variety of different workloads, including batch-style backend jobs and interactive and user-facing applications.” Based on this definition, a cloud allows workloads to be deployed and scaled out quickly through rapid provisioning of virtual or physical machines. The cloud supports redundant, self-recovering, highly scalable programming models that allow workloads to recover from many unavoidable hardware/software failures. Finally, the cloud system should be able to monitor resource use in real time to enable rebalancing of allocations when needed.
Internet Clouds
Cloud computing applies a virtualized platform with elastic resources on demand by provisioning hardware, software, and data sets dynamically (see Figure 1.18). The idea is to move desktop computing to a service-oriented platform using server clusters and huge databases at data centers. Cloud computing leverages its low cost and simplicity to benefit both users and providers. Machine virtualization has enabled such cost-effectiveness. Cloud computing intends to satisfy many user applications simultaneously. The cloud ecosystem must be designed to be secure, trustworthy, and dependable. Some computer users think of the cloud as a centralized resource pool. Others consider the cloud to be a server cluster which practices distributed computing over all the servers used.
The Cloud Landscape
Traditionally, a distributed computing system tends to be owned and operated by an autonomous administrative domain (e.g., a research laboratory or company) for on-premises computing needs. However, these traditional systems have encountered several performance bottlenecks: constant system maintenance, poor utilization, and increasing costs associated with hardware/software upgrades. Cloud computing as an on-demand computing paradigm resolves or relieves us from these problems. Figure 1.19 depicts the cloud landscape and major cloud players, based on three cloud service models. Chapters 4, 6, and 9 provide details regarding these cloud service offerings. Chapter 3 covers the relevant virtualization tools.
Infrastructure as a Service (IaaS)
- This model puts together infrastructures demanded by users—namely servers, storage, networks, and the data center fabric.
- The user can deploy and run on multiple VMs running guest OSes on specific applications.
- The user does not manage or control the underlying cloud infrastructure, but can specify when to request and release the needed resources.
Platform as a Service (PaaS)
- This model enables the user to deploy user-built applications onto a virtualized cloud platform.
- PaaS includes middleware, databases, development tools, and some runtime support such as Web 2.0 and Java.
- The platform includes both hardware and software integrated with specific programming interfaces.
- The
provider supplies the API and software tools (e.g., Java, Python, Web 2.0,
.NET). The user is freed from managing the cloud infrastructure.
Software as a Service (SaaS)
- This refers to browser-initiated application software over thousands of paid cloud customers.
- The SaaS model applies to business processes, industry applications, consumer relationship management (CRM), enterprise resources planning (ERP), human resources (HR), and collaborative applications.
- On the customer side, there is no upfront investment in servers or software licensing.
- On the provider side, costs are rather low, compared with conventional hosting of user applications.
Reasons to adapt the cloud for upgraded Internet applications and web services:
1. Desired location in areas with protected space and higher energy efficiency
2. Sharing of peak-load capacity among a large pool of users, improving overall utilization
3.Separation of infrastructure maintenance duties from domain-specific application development
4. Significant reduction in cloud computing cost, compared with traditional computing paradigms
5. Cloud computing programming and application development
6. Service and data discovery and content/service distribution
7. Privacy, security, copyright, and reliability issues
8. Service agreements, business models, and pricing policies
