Cloud load balancing

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Cloud load balancing is a type of load balancing that is performed in cloud computing. [1] Cloud load balancing is the process of distributing workloads across multiple computing resources. Cloud load balancing reduces costs associated with document management systems and maximizes availability of resources. It is a type of load balancing and not to be confused with Domain Name System (DNS) load balancing. While DNS load balancing uses software or hardware to perform the function, [2] cloud load balancing uses services offered by various computer network companies. [3]

Contents

Comparison With DNS load balancing

Cloud load balancing has an advantage over DNS load balancing as it can transfer loads to servers globally as opposed to distributing it across local servers. [3] In the event of a local server outage, cloud load balancing delivers users to the closest regional server without interruption for the user.

Cloud load balancing addresses issues relating to TTL reliance present during DNS load balancing. [4] DNS directives can only be enforced once in every TTL cycle and can take several hours if switching between servers during a lag or server failure. Incoming server traffic will continue to route to the original server until the TTL expires and can create an uneven performance as different internet service providers may reach the new server before other internet service providers. [4] Another advantage is that cloud load balancing improves response time by routing remote sessions to the best performing data centers. [1] [5]

Importance of Load Balancing

Cloud computing brings advantages in "cost, flexibility and availability of service users." [6] Those advantages drive the demand for Cloud services. The demand raises technical issues in Service Oriented Architectures and Internet of Services (IoS)-style applications, such as high availability and scalability. As a major concern in these issues, load balancing allows cloud computing to "scale up to increasing demands" [6] by efficiently allocating dynamic local workload evenly across all nodes. [7]

Load Balancing Techniques

Scheduling Algorithms

Opportunistic Load Balancing (OLB) is the algorithm that assigns workloads to nodes in free order. It is simple but does not consider the expected execution time of each node. [8] Load balance Min-Min (LBMM) assigns sub-tasks to the node which requires minimum execution time. [8]

Load Balancing Policies

Workload and Client Aware Policy (WCAP) is "implemented in a dis-centralized manner with low overhead." [9] It specifies the unique and special property (USP) of requests and computing nodes. With the information of USP, the schedule can decide the most suitable node to complete a request. WCAP makes the most of computing nodes by reducing their idle time. Also, it reduces performance time through searches based on content information.

A Comparative Study of Algorithms

Biased Random Sampling bases its job allocation on the network represented by a directed graph. For each execution node in this graph, in-degree means available resources and out-degree means allocated jobs. In-degree will decrease during job execution while out-degree will increase after job allocation.

Active Clustering is a self-aggregation algorithm to rewire the network.

The experiment result is that"Active Clustering and Random Sampling Walk predictably perform better as the number of processing nodes is increased" [6] while the Honeyhive algorithm does not show the increasing pattern.

Client-side Load Balancer Using Cloud Computing

Load balancer forwards packets to web servers according to different workloads on servers. However, it is hard to implement a scalable load balancer because of both the "cloud's commodity business model and the limited infrastructure control allowed by cloud providers." [10] Client-side Load Balancer (CLB) solve this problem by using a scalable cloud storage service. CLB allows clients to choose back-end web servers for dynamic content although it delivers static content.

Related Research Articles

<span class="mw-page-title-main">Client–server model</span> Distributed application structure in computing

The client–server model is a distributed application structure that partitions tasks or workloads between the providers of a resource or service, called servers, and service requesters, called clients. Often clients and servers communicate over a computer network on separate hardware, but both client and server may reside in the same system. A server host runs one or more server programs, which share their resources with clients. A client usually does not share any of its resources, but it requests content or service from a server. Clients, therefore, initiate communication sessions with servers, which await incoming requests. Examples of computer applications that use the client–server model are email, network printing, and the World Wide Web.

The Domain Name System (DNS) is a hierarchical and distributed naming system for computers, services, and other resources in the Internet or other Internet Protocol (IP) networks. It associates various information with domain names assigned to each of the associated entities. Most prominently, it translates readily memorized domain names to the numerical IP addresses needed for locating and identifying computer services and devices with the underlying network protocols. The Domain Name System has been an essential component of the functionality of the Internet since 1985.

In telecommunication, provisioning involves the process of preparing and equipping a network to allow it to provide new services to its users. In National Security/Emergency Preparedness telecommunications services, "provisioning" equates to "initiation" and includes altering the state of an existing priority service or capability.

<span class="mw-page-title-main">Load balancing (computing)</span> Set of techniques to improve the distribution of workloads across multiple computing resources

In computing, load balancing is the process of distributing a set of tasks over a set of resources, with the aim of making their overall processing more efficient. Load balancing can optimize the response time and avoid unevenly overloading some compute nodes while other compute nodes are left idle.

<span class="mw-page-title-main">Web hosting service</span> Service for hosting websites

A web hosting service is a type of Internet hosting service that hosts websites for clients, i.e. it offers the facilities required for them to create and maintain a site and makes it accessible on the World Wide Web. Companies providing web hosting services are sometimes called web hosts.

Scalability is the property of a system to handle a growing amount of work by adding resources to the system.

<span class="mw-page-title-main">Anycast</span> Network addressing and routing methodology

Anycast is a network addressing and routing methodology in which a single destination IP address is shared by devices in multiple locations. Routers direct packets addressed to this destination to the location nearest the sender, using their normal decision-making algorithms, typically the lowest number of BGP network hops. Anycast routing is widely used by content delivery networks such as web and DNS hosts, to bring their content closer to end users.

<span class="mw-page-title-main">Content delivery network</span> Layer in the internet ecosystem addressing bottlenecks

A content delivery network, or content distribution network (CDN), is a geographically distributed network of proxy servers and their data centers. The goal is to provide high availability and performance by distributing the service spatially relative to end users. CDNs came into existence in the late 1990s as a means for alleviating the performance bottlenecks of the Internet as the Internet was starting to become a mission-critical medium for people and enterprises. Since then, CDNs have grown to serve a large portion of the Internet content today, including web objects, downloadable objects, applications, live streaming media, on-demand streaming media, and social media sites.

Software multitenancy is a software architecture in which a single instance of software runs on a server and serves multiple tenants. Systems designed in such manner are "shared". A tenant is a group of users who share a common access with specific privileges to the software instance. With a multitenant architecture, a software application is designed to provide every tenant a dedicated share of the instance - including its data, configuration, user management, tenant individual functionality and non-functional properties. Multitenancy contrasts with multi-instance architectures, where separate software instances operate on behalf of different tenants.

<span class="mw-page-title-main">Edge computing</span> Distributed computing paradigm

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. Edge computing is an architecture rather than a specific technology, and a topology- and location-sensitive form of distributed computing.

<span class="mw-page-title-main">Fast flux</span> DNS evasion technique against origin server fingerprinting.

Fast flux is a domain name system (DNS) based evasion technique used by cyber criminals to hide phishing and malware delivery websites behind an ever-changing network of compromised hosts acting as reverse proxies to the backend botnet master—a bulletproof autonomous system. It can also refer to the combination of peer-to-peer networking, distributed command and control, web-based load balancing and proxy redirection used to make malware networks more resistant to discovery and counter-measures.

<span class="mw-page-title-main">Cloud computing</span> Form of shared Internet-based computing

Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each of which is a data center. Cloud computing relies on sharing of resources to achieve coherence and typically uses a "pay as you go" model, which can help in reducing capital expenses but may also lead to unexpected operating expenses for users.

Cloud testing is a form of software testing in which web applications use cloud computing environments to simulate real-world user traffic.

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<span class="mw-page-title-main">Cloud computing architecture</span> Overview about the cloud computing architecture

Cloud computing architecture refers to the components and subcomponents required for cloud computing. These components typically consist of a front end platform, back end platforms, a cloud based delivery, and a network. Combined, these components make up cloud computing architecture.

<span class="mw-page-title-main">Hazelcast</span> In-memory data grid

In computing, Hazelcast IMDG is an open source in-memory data grid based on Java. It is also the name of the company developing the product. The Hazelcast company is funded by venture capital and headquartered in Palo Alto, California.

A distributed file system for cloud is a file system that allows many clients to have access to data and supports operations on that data. Each data file may be partitioned into several parts called chunks. Each chunk may be stored on different remote machines, facilitating the parallel execution of applications. Typically, data is stored in files in a hierarchical tree, where the nodes represent directories. There are several ways to share files in a distributed architecture: each solution must be suitable for a certain type of application, depending on how complex the application is. Meanwhile, the security of the system must be ensured. Confidentiality, availability and integrity are the main keys for a secure system.

Kubernetes is an open-source container orchestration system for automating software deployment, scaling, and management. Originally designed by Google, the project is now maintained by the Cloud Native Computing Foundation.

Autoscaling, also spelled auto scaling or auto-scaling, and sometimes also called automatic scaling, is a method used in cloud computing that dynamically adjusts the amount of computational resources in a server farm - typically measured by the number of active servers - automatically based on the load on the farm. For example, the number of servers running behind a web application may be increased or decreased automatically based on the number of active users on the site. Since such metrics may change dramatically throughout the course of the day, and servers are a limited resource that cost money to run even while idle, there is often an incentive to run "just enough" servers to support the current load while still being able to support sudden and large spikes in activity. Autoscaling is helpful for such needs, as it can reduce the number of active servers when activity is low, and launch new servers when activity is high. Autoscaling is closely related to, and builds upon, the idea of load balancing.

<span class="mw-page-title-main">Oracle Cloud</span> Cloud computing service

Oracle Cloud is a cloud computing service offered by Oracle Corporation providing servers, storage, network, applications and services through a global network of Oracle Corporation managed data centers. The company allows these services to be provisioned on demand over the Internet.

References

  1. 1 2 Chee, Brian J.S. (2010). Cloud Computing: Technologies and Strategies of the Ubiquitous Data Center. CRC Press. ISBN   9781439806173.
  2. ↑ Xu, Cheng-Zhong (2005). Scalable and Secure Internet Services and Architecture. CRC Press. ISBN   9781420035209.
  3. 1 2 "Research Report - In Demand – The Culture of Online Service Provision". Citrix. 14 October 2013. Archived from the original on 23 January 2014. Retrieved 30 January 2014.
  4. 1 2 Furht, Borko (2010). Handbook of Cloud Computing. Springer. ISBN   9781441965240.
  5. ↑ Nolle, Tom. "Designing public cloud applications for a hybrid cloud future". Tech Target. Retrieved 30 January 2014.
  6. 1 2 3 Randles, Martin, David Lamb, and A. Taleb-Bendiab. "A comparative study into distributed load balancing algorithms for cloud computing." Advanced Information Networking and Applications Workshops (WAINA), 2010 IEEE 24th International Conference on. IEEE, 2010.
  7. ↑ Ferris, James Michael. "Methods and systems for load balancing in cloud-based networks." U.S. Patent Application 12/127,926.
  8. 1 2 Wang, S. C.; Yan, K. Q.; Liao, W. P.; Wang, S. S. (2010), "Towards a load balancing in a three-level cloud computing network", Proceedings of the 3rd International Conference on Computer Science and Information Technology (ICCSIT), IEEE: 108–113, ISBN   978-1-4244-5537-9
  9. ↑ Kansal, Nidhi Jain, and Inderveer Chana. "Cloud load balancing techniques: A step towards green computing." IJCSI International Journal of Computer Science Issues 9.1 (2012): 1694-0814.
  10. ↑ Wee, Sewook, and Huan Liu. "Client-side load balancer using cloud." Proceedings of the 2010 ACM Symposium on Applied Computing. ACM, 2010.
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