2. Group Based Management of Distributed File Caches
Amer,Long,Burns

3. Prefetching vs. Automated Grouping “Reducing Access Latency”
Dynamic File Grouping
Prefetching vs. Automated Grouping
“Reducing Access Latency”
Implicit prefetching
Increasing retention priority
Modelling allowing overlapping partitions
Why? Scenarios.

4. Success and accuracy dependent on model and strength of relationships.
Edge priority
Predicting file access behavior:
Frequency or Recency ?
Location?
Single or group of files?
Input data?

5. Hot data items where access costs are less.
Tracking sequence, not the time.
Aggregating Cache
Successors: Immediate and Transitive
Group [g] for retrieval.
Server maintains immediate successors
Client then uses LRU replacement in cache, requested at the front followed by [g –1].

6. Server Side: Comparing LRU, LFU, Dgrouping
Client Side:
Demand fetches,Cache hit ratios,Miss rates.
Server Side: Comparing LRU, LFU, Dgrouping
Statistics are accessed piggy backed with client file requests.
Client cache -> Server cache, drop in hit rate for server cache.
Interdependency among file access events
Comparisons based on recency vs. frequency for maintaining successors

7. Associating files with successors:
Sequences or single files?
CAB,CDB….successors are A and D
Tracking sequences: More metadata, reduce likelihood of repeated successors.
Single file: Greater predictability
Ideal may be a combination of frequency and recency.

8. Efficient Massive Sharing of Content Among Peers
Triantafillou, Xiruhaki, Koubarakis

9. Exploit semantics of documents, construct clusters.
Goal is to achieve
Global load balancing
Short response time
Exploit semantics of documents, construct clusters.
Assumptions:
Known popularity
Documents accompanied by key words

10. Idea is to impose order in chaos!
Goal is then to achieve
Inter and Intra cluster load balancing.
Idea is to impose order in chaos!
Each doc is associated with a popularity.
Requests can either retrieve or publish

11. Response time bound by no. of nodes in the cluster.
Processing:
Obtain semantic category through keywords in the request.
Associate categories to clusters using metadata present locally.
Forward to cluster.
Intra cluster load balancing: forwarding requests to a random node each time.
Response time bound by no. of nodes in the cluster.

12. Inter cluster load balancing: NP complete Assumptions:
All peers have the same capacities
Enough storage space to store documents of contributed categories
Q: Partition N into k clusters,such that
If two docs belong to same category, then the host nodes also belong to the sme cluster.
Clusters have equal normalized probabilities.

13. Partitioning into clusters with nearly equal normalized probabilities.
Using a greedy algorithm, MinDiff
All clusters initially empty
For each semantic category, assign to the cluster which min total diff of popularities among clusters.

14. Discussions