6,109 research outputs found
A cluster-based decentralized job dispatching for the large-scale cloud.
The remarkable development of cloud computing in the past few years, and its proven ability to handle web hosting
workloads, is prompting researchers to investigate whether clouds are suitable to run large-scale computations. Cloud
load balancing is one of the solution to provide reliable and scalable cloud services. Especially, load balancing for the
multimedia streaming requires dynamic and real-time load balancing strategies. With this context, this paper aims to
propose an Inter Cloud Manager (ICM) job dispatching algorithm for the large-scale cloud environment. ICM mainly
performs two tasks: clustering (neighboring) and decision-making. For clustering, ICM uses Hello packets that observe
and collect data from its neighbor nodes, and decision-making is based on both the measured execution time and
network delay in forwarding the jobs and receiving the result of the execution. We then run experiments on a
large-scale laboratory test-bed to evaluate the performance of ICM, and compare it with well-known decentralized
algorithms such as Ant Colony, Workload and Client Aware Policy (WCAP), and the Honey-Bee Foraging Algorithm
(HFA). Measurements focus in particular on the observed total average response time including network delay in
congested environments. The experimental results show that for most cases, ICM is better at avoiding system
saturation under the heavy load.N/
DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling
The dynamic provisioning of virtualized resources offered by cloud computing
infrastructures allows applications deployed in a cloud environment to
automatically increase and decrease the amount of used resources. This
capability is called auto-scaling and its main purpose is to automatically
adjust the scale of the system that is running the application to satisfy the
varying workload with minimum resource utilization. The need for auto-scaling
is particularly important during workload peaks, in which applications may need
to scale up to extremely large-scale systems.
Both the research community and the main cloud providers have already
developed auto-scaling solutions. However, most research solutions are
centralized and not suitable for managing large-scale systems, moreover cloud
providers' solutions are bound to the limitations of a specific provider in
terms of resource prices, availability, reliability, and connectivity.
In this paper we propose DEPAS, a decentralized probabilistic auto-scaling
algorithm integrated into a P2P architecture that is cloud provider
independent, thus allowing the auto-scaling of services over multiple cloud
infrastructures at the same time. Our simulations, which are based on real
service traces, show that our approach is capable of: (i) keeping the overall
utilization of all the instantiated cloud resources in a target range, (ii)
maintaining service response times close to the ones obtained using optimal
centralized auto-scaling approaches.Comment: Submitted to Springer Computin
Load Balancing Techniques in Cloud Computing
As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. The top challenges and Issues faced by cloud Computing is Security, Availability, Performance etc. The issue availability is mainly related to efficient load balancing, resource utilization & live migration of data in the server. In clouds, load balancing, as a method, is applied across different data centres to ensure the network availability by minimizing use of computer hardware, software failures and mitigating recourse limitations. Load Balancing is essential for efficient operations in distributed environments. Hence this paper presents the various existing load balancing Technique in Cloud Computing based on different parameters
- …