1. PARALLEL AND DISTRIBUTED
COMPUTING
• DATE OF SUBMISSION: DECEMBER 2, 2023
•
SUBMITTED BY: ABDULLAH
JAMSHAID
•
SUBMITTED TO: DR. GULZAR AHMED
•
CLASS: BS CS 5TH AFTER
ADP
2. LEVERAGING PARALLEL
AND DISTRIBUTED
COMPUTING IN E-
COMMERCE DATA
PROCESSING
• E-COMMERCE THRIVES
ON DATA, AND MANAGING
VAST DATABASES IS
CRUCIAL. THIS ASSIGNMENT
EXPLORES USING PARALLEL
AND DISTRIBUTED
COMPUTING CONCEPTS TO
EFFICIENTLY HANDLE
MASSIVE DATA VOLUMES IN
E-COMMERCE PROJECTS.
3. • DATA PARTITIONING
• HORIZONTAL PARTITIONING (SHARDING): DIVIDING DATA LOGICALLY.
• CONSIDERATION FOR DATA SKEWNESS: PREVENTING UNEVEN LOADS DUE TO DATA
IMBALANCE.
• TASK PARALLELIZATION
• UTILIZING PARALLEL PROCESSING FRAMEWORKS: IMPLEMENTING SYSTEMS FOR PARALLEL
EXECUTION.
• OPTIMIZATION THROUGH PIPELINE PROCESSING: DESIGNING SEQUENTIAL PARALLEL
ARCHITECTUARES.
• FAULT TOLERANCE
• REPLICATION AND REDUNDANCY: CREATING DATA COPIES ACROSS NODES.
• CHECKPOINTING AND RECOVERY: SAVING INTERMEDIATE STATES FOR FAILURE RECOVERY.
• SCALABILITY
• CLOUD-BASED INFRASTRUCTURE: USING SERVICES FOR FLEXIBLE RESOURCE ALLOCATION.
• AUTO-SCALING AND LOAD BALANCING: SETTING UP POLICIES FOR RESOURCE
MANAGEMENT.
4. BENEFITS:
• PERFORMANCE ENHANCEMENT: REDUCING COMPUTATION TIME BY HARNESSING
MULTIPLE RESOURCES SIMULTANEOUSLY.
• RESOURCE OPTIMIZATION: REDUCING IDLE TIME AND OPERATIONAL COSTS THROUGH
EFFICIENT RESOURCE UTILIZATION.
• ADAPTABILITY: FLEXIBILITY TO ACCOMMODATE GROWING DATA VOLUMES AND
EVOLVING BUSINESS NEEDS.
• EFFICIENCY AND COST-EFFECTIVENESS: OPTIMIZING RESOURCE USE AND LEVERAGING
CLOUD-BASED INFRASTRUCTURE FOR COST SAVINGS.
• DATA-DRIVEN DECISION MAKING: DEEPER INSIGHTS ENABLE STRATEGIC DECISIONS
BASED ON CUSTOMER BEHAVIOR.
• PERSONALIZED CUSTOMER EXPERIENCES: TAILORING RECOMMENDATIONS AND
MARKETING BASED ON INDIVIDUAL PREFERENCES.
• OPERATIONAL EFFICIENCY: PREDICTIVE ANALYTICS AIDING INVENTORY MANAGEMENT
AND SUPPLY CHAIN OPTIMIZATION.
5. DISADVANTAGES:
• INCREASED COMPLEXITY: DESIGNING AND MANAGING DISTRIBUTED SYSTEMS
REQUIRE SPECIALIZED EXPERTISE.
• COMMUNICATION OVERHEAD: FREQUENT NODE COMMUNICATION CAN CAUSE
PERFORMANCE BOTTLENECKS.
• SECURITY CONCERNS: DISTRIBUTED DATA INTRODUCES ADDITIONAL SECURITY
CHALLENGES.
• DEBUGGING DIFFICULTIES: TROUBLESHOOTING IN DISTRIBUTED SYSTEMS IS
COMPLEX DUE TO DISTRIBUTED NATURE.
• COST CONSIDERATIONS: INITIAL SETUP AND MANAGEMENT OF DISTRIBUTED
SYSTEMS CAN BE COSTLY.
• VENDOR LOCK-IN: DEPENDENCE ON SPECIFIC CLOUD PLATFORMS CAN LIMIT
FUTURE CHOICES.
6. CONCLUSION:
• PARALLEL AND DISTRIBUTED
COMPUTING ARE POWERFUL TOOLS FOR
E-COMMERCE. BY EFFICIENTLY
HANDLING MASSIVE DATA VOLUMES,
BUSINESSES CAN EXTRACT VALUABLE
INSIGHTS, ENHANCE CUSTOMER
EXPERIENCES, AND OPTIMIZE
OPERATIONS. EMBRACING THESE
TECHNOLOGIES IS CRUCIAL FOR E-
COMMERCE TO THRIVE IN A DATA-
CENTRIC ENVIRONMENT.
