v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers †
Abstract
:1. Introduction
2. Related Work
2.1. VM Management in Cloud Datacenters
2.2. SDN Role in Datacenter Optimization
3. Application-Awareness Concepts in Cloud Datacenters
4. Data Center Design and the v-Mapper System Model
4.1. VM Placement and the Data Center Model
Algorithm 1. VM placement | |
1: | Input: Load DP on SN |
2: | Output: VM placement decision |
3: | Let CN denote Compute Node of a cloud network |
4: | Let tCN,i denote service response time of a CNi |
5: | Let least denote least response time of all CN |
6: | Let j denote the CN with least response time |
7: | Calculate tR for each CN as |
8: | tCN,i = |
9: | i ⟵ 0 |
10: | j ⟵ 0 |
11: | least ⟵ tCN,0 |
12: | while I < n do |
13: | if least > tCN,i then |
14: | least ⟵ tCN,i |
15: | j ⟵ i |
16: | end if |
17: | i ⟵ i + 1 |
18: | end while |
19: | Place VM on CN j |
20: | Exit |
4.2. VM Workload Admission
- (1)
- First, we determine the factor and sub-factor weights for cloud services through an evaluation index (U). The factor and sub-factor weights are assigned through priority criteria of AHP. A simple case example is presented in [1] and can be used to measure functions influencing a cloud service’s resource management concerns.
- (2)
- “Not all clouds are created equal”; therefore, we create a set of comments (V) to describe the evaluation of cloud services by using phrases such as “Acceptable”, “Constrained”, etc. These are determined based on Saaty’s 1 to 9 scale [26] to describe the preferences between alternatives as being either equally, moderately, strongly, very strongly or extremely preferred.
- (3)
- To provide a comprehensive assessment methodology, we create an evaluation matrix (R) from U to V, where each factor ui (i ≤ n) can be written as a fuzzy vector Ri ∈ μ(V). Mathematically, this fuzzy relationship can be expressed as:The evaluated result of Equation (6) should match the normalized conditions, because the sum of the weight of the vector is 1 (i.e., for i, ri1 + ri2 + ri3 +…….. + rim = 1).
- (4)
- A factor assigned to a number in a computation system reflects its importance.The greater the weight of a factor is, the more importance it has in the system. We, therefore, determine the factor weight (FW) of each factor in the evaluation index (U) system.
- (5)
- We obtain the evaluation result (E) through the product of the factor weight (FW) and the evaluation matrix (R). This can be denoted as E = FW(R) = (E1, E2, E3,…, Em). Finally, the evaluated weight now can be assigned to the respective application.
Algorithm 2. VM workload admission | |
1: | Input: Workload admission request |
2: | Output: Workload admission decision |
3: | Let resava denote the available resources for a VM |
4: | Let resreq denote the requested resources by a VM |
5: | Let queue denote the request queue |
6: | while (!queue.isEmpty()) |
7: | { |
8: | req = queue.firstRequestResource(); |
9: | if (resreq ≤ resava) |
10: | { |
11: | resava = resava − resreq |
12: | queue.pop(); |
13: | } |
14: | Else |
15: | { |
16: | sendMessage(“resources inadequate”); |
17: | queue.pop(); |
18: | } |
19: | } |
4.3. VM Scheduling Scheme
Algorithm 3. VM scheduling policy | |
Input: qi: represent a new request i; Rqi: the (required) resources for a service request i; Rt: system threshold for the resource, Pqi: priority of request i; N: request number in a memory buffer br. | |
Output: scheduling decision | |
1: | Let priority = |
2: | while Rqi ≤ Rt do |
3: | Assign request qi its calculated priority value |
4: | Push qi to br |
5: | end while |
6: | /* Sort buffer requests by priority in descending order */ |
7: | for I ⟵ 0 to N do |
8: | for j ⟵ 0 to N-2-i do |
9: | if Pqj < Pq(j+1) do |
10: | Pqj ↔ Pq(j+1) |
11: | end if |
12: | end for |
13: | end for |
14: | /* Process all sorted VM requests */ |
15: | for i ⟵ 0 to N do |
16: | Process(qi) |
17: | end for |
18: | exit |
5. Performance Test Bed and Simulation Environment
5.1. Baseline Strategies and Compared Scenarios
5.2. Simulation Set-Up and Settings
6. Performance Results and Analysis
6.1. Impact on Data Center Topologies
6.2. Performance Cost
6.3. VM Resource Occupancy
7. Conclusions and Future Work
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Notation | Description |
---|---|
SN | Storage Node |
DP | Data Packets |
U | Evaluation index |
V | Set of comments |
R | Evaluation matrix |
FW | Factor Weight |
P | Probability |
SR | Shared Resources (Available) |
SFL | Shared Fair Load |
Qreq | Scheduling Request Query |
Rt | System Threshold Capacity |
Br | Buffered requests |
Pqi | Priority value |
B | Adjacency matrix between storage and cloud node matrices as i and j |
N | Number of aggregate compute nodes |
Shared fair load SFL of the shared resource SR |
Size | Request Range |
---|---|
Uniform small | 30 percent |
Uniform large | 100 percent |
Random | 0 to 100 percent |
Bimodel demand | Normal distributions N (0:3;0:1) and N (0:7;0:1) with equal probability |
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Abbasi, A.A.; Jin, H. v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers. Future Internet 2018, 10, 90. https://doi.org/10.3390/fi10090090
Abbasi AA, Jin H. v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers. Future Internet. 2018; 10(9):90. https://doi.org/10.3390/fi10090090
Chicago/Turabian StyleAbbasi, Aaqif Afzaal, and Hai Jin. 2018. "v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers" Future Internet 10, no. 9: 90. https://doi.org/10.3390/fi10090090