THERMAL-AWARE SCHEDULING OF BATCH JOBS IN GEOGRAPHICALLY DISTRIBUTED DATA CENTERS

THERMAL-AWARE SCHEDULING OF BATCH JOBS IN  GEOGRAPHICALLY DISTRIBUTED DATA CENTERS

                             CLICK HERE TO VIEW THE OUTPUT

ABSTRACT:

Decreasing the soaring energy cost is imperative in large data centers. Meanwhile, limited computational resources need to be fairly allocated among different organizations. Latency is another major concern for resource management. Nevertheless, energy cost, resource allocation fairness, and latency are important but often contradicting metrics on scheduling data center workloads. Moreover, with the ever-increasing power density, data center operation must be judiciously optimized to prevent server overheating. In this paper, we explore the benefit of electricity price variations across time and locations. We study the problem of scheduling batch jobs to multiple geographically-distributed data centers. We propose a provably-efficient online scheduling algorithm—GreFar—which optimizes the energy cost and fairness among different organizations subject to queuing delay constraints, while satisfying the maximum server inlet temperature constraints. GreFar does not require any statistical information of workload arrivals or electricity prices. We prove that it can minimize the cost arbitrarily close to that of the optimal offline algorithm with future information. Moreover, we compare the performance of GreFar with ones of a similar algorithm, referred to as T-unaware, that is not able to consider the server inlet temperature in the scheduling process. We prove that GreFar is able to save up to 16 percent of energy-fairness cost with respect to
T-unaware.
EXISTING SYSTEM:

Better energy efficiency of servers and lower electricity prices are both important in reducing the energy cost. Given the heterogeneity of servers in terms of energy efficiency and the diversity of electricity prices over geographically distributed data centers and over time, the key idea is to preferentially shift power draw to energy efficient servers and to places and times offering cheaper electricity prices. Moreover, with the ever-increasing power density generating an excessive amount of heat.

DISADVANTAGES OF EXISTING SYSTEM:

v Thermal management in data centers is becoming imperatively important for preventing server overheating that could potentially induce server damages and huge economic losses.

v Reducing energy cost by means of overloading servers in data centers where the electricity price is low may not be a viable solution, since this may result in a higher server temperature that imposes serious concerns for system reliability.

PROPOSED SYSTEM:

We propose a practical yet provably-efficient online scheduling algorithm “GreFar” to solve this problem. Our algorithm does not require any prior knowledge of the system statistics (which can even be non-stationary) or any Recommended for prediction on future job arrivals and server availability. Moreover, it is computationally efficient and easy to implement in large practical systems. GreFar constructs and solves an online optimal problem based on the current job queue lengths, server availability and temperature, and electricity prices; the solution is proven to offer close to the offline optimal performance with future information. More precisely, given a cost-delay parameter V 0, GreFar is Oð1=V Þ-optimal with respect to the average (energy-fairness) cost against the offline optimal algorithm while bounding the queue length by OðV Þ. Without considering fairness, the energy-fairness cost solely represents the energy cost, while with fairness taken into account, it is an affine combination of energy cost and fairness score (which is obtained through a fairness function).

ADVANTAGES OF PROPOSED SYSTEM:

v Optimally distributing the workloads to facilitate heat recirculation and avoid overheating has to be incorporated in data center operation.

v Our algorithm is associated with two control parameters, i.e., cost-delay parameter and energy-fairness parameter, which can be appropriately tuned to provide a desired performance tradeoff among energy, fairness and queuing delay.

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

ü Processor                  -        Pentium –IV

ü Speed                        -        1.1 Ghz

ü RAM                         -        512 MB(min)

ü Hard Disk                 -        40 GB

ü Key Board                -        Standard Windows Keyboard

ü Mouse                       -        Two or Three Button Mouse

ü Monitor                     -        LCD/LED

SOFTWARE REQUIREMENTS:

•         Operating system :         Windows XP.

•         Coding Language :         C# .Net

•         Data Base             :         SQL Server 2005

•         Tool                     :         VISUAL STUDIO 2008.

REFERENCE:

Marco Polverini, Antonio Cianfrani, Shaolei Ren, Member, IEEE, and Athanasios V. Vasilakos “THERMAL-AWARE SCHEDULING OF BATCH JOBS IN

GEOGRAPHICALLY DISTRIBUTED DATA CENTERS” IEEE TRANSACTIONS ON CLOUD COMPUTING, VOL. 2, NO. 1, JANUARY-MARCH 2014